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Task-Specific Temporal Node Embedding

EasyChair Preprint no. 5807

9 pagesDate: June 15, 2021


Graph embedding aims to learn a representation of graphs' nodes in a latent low-dimensional space. The purpose is to encode the graph’s structural information. While the majority of real-world networks is dynamic, literature generally focuses on static networks and overlooks evolution patterns. In a previous article entitled "TemporalNode2vec: Temporal Node Embedding in Temporal Networks", we introduced a dynamic graph embedding method that learns continuous time-aware vertex representations. In this paper, we adapt TemporalNode2vec to tackle especially the node classification related tasks. Overall, we prove that task-specific embedding improves data efficiency significantly comparing to task-agnostic embedding.

Keyphrases: Dynamic network embeddings, graph representation learning, latent representations

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Mounir Haddad and Cécile Bothorel and Philippe Lenca and Dominique Bedart},
  title = {Task-Specific Temporal Node Embedding},
  howpublished = {EasyChair Preprint no. 5807},

  year = {EasyChair, 2021}}
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